
Adversarial Classification: Necessary conditions and geometric flows
We study a version of adversarial classification where an adversary is e...
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Lipschitz regularity of graph Laplacians on random data clouds
In this paper we study Lipschitz regularity of elliptic PDEs on geometri...
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Semidiscrete optimization through semidiscrete optimal transport: a framework for neural architecture search
In this paper we introduce a theoretical framework for semidiscrete opt...
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Traditional and accelerated gradient descent for neural architecture search
In this paper, we introduce two algorithms for neural architecture searc...
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From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds
In this work we study statistical properties of graphbased clustering a...
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DataDriven Forward Discretizations for Bayesian Inversion
This paper suggests a framework for the learning of discretizations of e...
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Improved spectral convergence rates for graph Laplacians on epsilongraphs and kNN graphs
In this paper we improve the spectral convergence rates for graphbased ...
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Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis
Several data analysis techniques employ similarity relationships between...
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Geometric structure of graph Laplacian embeddings
We analyze the spectral clustering procedure for identifying coarse stru...
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A maximum principle argument for the uniform convergence of graph Laplacian regressors
We study asymptotic consistency guarantees for a nonparametric regressi...
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Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning
The aim of this paper is to provide new theoretical and computational un...
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Spatial extreme values: variational techniques and stochastic integrals
This work employs variational techniques to revisit and expand the const...
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Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the LaplaceBeltrami operator
We study the convergence of the graph Laplacian of a random geometric gr...
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On the Consistency of Graphbased Bayesian Learning and the Scalability of Sampling Algorithms
A popular approach to semisupervised learning proceeds by endowing the ...
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Continuum Limit of Posteriors in Graph Bayesian Inverse Problems
We consider the problem of recovering a function input of a differential...
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GromovHausdorff limit of Wasserstein spaces on point clouds
We consider a point cloud X_n := { x_1, ..., x_n } uniformly distributed...
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Variational limits of kNN graph based functionals on data clouds
We consider i.i.d. samples x_1, ..., x_n from a measure ν with density s...
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A new analytical approach to consistency and overfitting in regularized empirical risk minimization
This work considers the problem of binary classification: given training...
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A variational approach to the consistency of spectral clustering
This paper establishes the consistency of spectral approaches to data cl...
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Consistency of Cheeger and Ratio Graph Cuts
This paper establishes the consistency of a family of graphcutbased al...
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Continuum limit of total variation on point clouds
We consider point clouds obtained as random samples of a measure on a Eu...
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Nicolas Garcia Trillos
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